Expert and intelligent training systems. Expert training systems. Database management systems and expert systems
Topic 2.3. Presentation software and office programming basics
Topic 2.4.
2.4.11. Training database with the main button form "Training_students" - Download
Database management systems and expert systems
2.4. Database management systems and expert systems
2.4.10. Expert and learning systems
Expert systems are one of the main applications artificial intelligence. Artificial intelligence is one of the branches of computer science that deals with the problems of hardware and software modeling of those types human activity who are considered intelligent.
The results of research on artificial intelligence are used in intelligent systems that are capable of solving creative problems belonging to a specific subject area, knowledge about which is stored in the memory (knowledge base) of the system. Artificial intelligence systems are focused on solving a large class of problems, which include the so-called partially structured or unstructured tasks (weakly formalizable or unformalizable tasks).
Information systems used to solve semi-structured problems are divided into two types:
- Creating management reports (performing data processing: searching, sorting, filtering). Decisions are made based on the information contained in these reports.
- Developing possible solution alternatives. Decision making comes down to choosing one of the proposed alternatives.
Information systems that develop solution alternatives can be model or expert:
- Model information systems provide the user with models (mathematical, statistical, financial, etc.) that help ensure the development and evaluation of solution alternatives.
- Expert information systems provide the development and assessment of possible alternatives by the user through the creation of systems based on knowledge obtained from specialist experts.
Expert systems are computer programs that accumulate the knowledge of specialists - experts in specific subject areas, which are designed to obtain acceptable solutions in the process of information processing. Expert systems transform the experience of experts in any particular field of knowledge into the form of heuristic rules and are intended for consultation of less qualified specialists.
It is known that knowledge exists in two forms: collective experience and personal experience. If a subject area is represented by collective experience (for example, higher mathematics), then this subject area does not need expert systems. If in a subject area most of the knowledge is personal experience high-level specialists and this knowledge is poorly structured, then such an area needs expert systems. Modern expert systems have found wide application in all spheres of the economy.
The knowledge base is the core of the expert system. The transition from data to knowledge is a consequence of the development of information systems. Databases are used to store data, and knowledge bases are used to store knowledge. Databases, as a rule, store large amounts of data with a relatively low cost, while knowledge bases store small but expensive information sets.
A knowledge base is a body of knowledge described using the selected form of its presentation. Filling the knowledge base is one of the most complex tasks, which is associated with the choice of knowledge, its formalization and interpretation.
The expert system consists of:
- a knowledge base (as part of working memory and a rule base), designed to store initial and intermediate facts in working memory (it is also called a database) and store models and rules for manipulating models in the rule base;
- a problem solver (interpreter), which ensures the implementation of a sequence of rules for solving a specific problem based on facts and rules stored in databases and knowledge bases;
- explanation subsystem allows the user to get answers to the question: “Why did the system make this decision?”;
- a knowledge acquisition subsystem designed to both add new rules to the knowledge base and modify existing rules;
- user interface, a set of programs that implement the user’s dialogue with the system at the stage of entering information and obtaining results.
Expert systems differ from traditional data processing systems in that they typically use symbolic representation, symbolic inference, and heuristic search for solutions. For solving weakly formalizable or non-formalizable problems, neural networks or neurocomputers are more promising.
The basis of neurocomputers is neural networks - hierarchical organized parallel connections of adaptive elements - neurons, which ensure interaction with objects real world just like the biological nervous system.
Great successes in the use of neural networks have been achieved in the creation of self-learning expert systems. The network is configured, i.e. train by passing all known solutions through it and achieving the required answers at the output. The setup consists of selecting the parameters of the neurons. Often they use a specialized training program that trains the network. After training, the system is ready for operation.
If an expert system is preliminarily loaded with knowledge by its creators, a certain form, then in neural networks it is unknown even to developers how knowledge is formed in its structure in the process of learning and self-learning, i.e. the network is a “black box”.
Neurocomputers, as artificial intelligence systems, are very promising and can be endlessly improved in their development.
Currently, artificial intelligence systems in the form of expert systems and neural networks are widely used in solving financial and economic problems.
Expert systems are one of the main applications of artificial intelligence. Artificial intelligence is one of the branches of computer science that deals with the problems of hardware and software modeling of those types of human activities that are considered intellectual.
The results of research on artificial intelligence are used in intelligent systems that are capable of solving creative problems belonging to a specific subject area, knowledge about which is stored in the memory (knowledge base) of the system. Artificial intelligence systems are focused on solving a large class of problems, which include the so-called partially structured or unstructured tasks (weakly formalizable or unformalizable tasks).
Information systems used to solve semi-structured problems are divided into two types:
Creating management reports (performing data processing: searching, sorting, filtering). Decisions are made based on the information contained in these reports.
Developing possible solution alternatives. Decision making comes down to choosing one of the proposed alternatives.
Information systems that develop solution alternatives can be model or expert:
Model information systems provide the user with models (mathematical, statistical, financial, etc.) that help ensure the development and evaluation of solution alternatives.
Expert information systems provide the development and assessment of possible alternatives by the user through the creation of systems based on knowledge obtained from specialist experts.
Expert systems are computer programs that accumulate the knowledge of specialists - experts in specific subject areas, which are designed to obtain acceptable solutions in the process of information processing. Expert systems transform the experience of experts in any particular field of knowledge into the form of heuristic rules and are intended for consultation of less qualified specialists.
It is known that knowledge exists in two forms: collective experience and personal experience. If a subject area is represented by collective experience (for example, higher mathematics), then this subject area does not need expert systems. If in a subject area most of the knowledge is the personal experience of high-level specialists and this knowledge is weakly structured, then such an area needs expert systems. Modern expert systems have found wide application in all spheres of the economy.
The knowledge base is the core of the expert system. The transition from data to knowledge is a consequence of the development of information systems. Databases are used to store data, and knowledge bases are used to store knowledge. Databases, as a rule, store large amounts of data with a relatively low cost, while knowledge bases store small but expensive information sets.
A knowledge base is a body of knowledge described using the selected form of its presentation. Filling the knowledge base is one of the most difficult tasks, which is associated with the selection of knowledge, its formalization and interpretation.
The expert system consists of:
knowledge base (as part of working memory and a rule base), designed for storing initial and intermediate facts in working memory (also called a database) and storing models and rules for manipulating models in the rule base
problem solver (interpreter), which provides the implementation of a sequence of rules for solving a specific problem based on facts and rules stored in databases and knowledge bases
explanation subsystem allows the user to get answers to the question: “Why did the system make this decision?”
a knowledge acquisition subsystem designed to both add new rules to the knowledge base and modify existing rules.
user interface, a set of programs that implement the user’s dialogue with the system at the stage of entering information and obtaining results.
Expert systems differ from traditional data processing systems in that they typically use symbolic representation, symbolic inference, and heuristic search for solutions. For solving weakly formalizable or non-formalizable problems, neural networks or neurocomputers are more promising.
The basis of neurocomputers is neural networks - hierarchical organized parallel connections of adaptive elements - neurons, which ensure interaction with objects of the real world in the same way as the biological nervous system.
Great successes in the use of neural networks have been achieved in the creation of self-learning expert systems. The network is configured, i.e. train by passing all known solutions through it and achieving the required answers at the output. The setup consists of selecting the parameters of the neurons. Often they use a specialized training program that trains the network. After training, the system is ready for operation.
If in an expert system its creators pre-load knowledge in a certain form, then in neural networks it is unknown even to the developers how knowledge is formed in its structure in the process of learning and self-learning, i.e. the network is a “black box”.
Neurocomputers, as artificial intelligence systems, are very promising and can be endlessly improved in their development. Currently, artificial intelligence systems in the form of expert systems and neural networks are widely used in solving financial and economic problems.
| " |
Expert training system
Introduction
Currently, due to the rapid development of Internet technologies, more and more new interactive services are appearing for Internet and Intranet -networks, such as distance learning. The distance learning system is a fairly popular form of education in the world in those countries in which there is a fairly high level of development of means of communication based on computer technology. Training of modern specialists requires organization educational process using these new information technologies and using knowledge-based systems - expert systems (ES).
The use of ES for assessing the level of knowledge of students in testing systems determines an important block of computer programs - expert training systems (ETS).
Expert learning systems are computer programs, having the main components of the ES, but in which the explanation component is additionally expanded. Such systems are based both on the knowledge of software experts and on the knowledge of teaching methodology experts. In addition, they have a component of presentation adaptation educational material to the student depending on his preparedness. And at a minimum, there are several learning strategies, the level of detail of which depends on the student’s activity in dialogue with the system.
The use of EOS as a testing tool to determine the quality of knowledge of a student is also of great importance in teaching. Since during such testing the student is not influenced by a subjective factor, that is, the test results do not depend on the personal characteristics of the examiner and the person being tested. And the use of uniform tests allows the teacher to objectively assess the level of preparation of students.
1. Relevance of the topic
Due to the widespread use of computers, the role of computer training, the methodology of which increases the student’s intellectual abilities and independence in decision-making. And such qualities are most in demand in a competitive economy and contribute to educationalprofessional growth. There are problems creating effective systems training, as well as the creation of new forms and ways of presenting educational material, the search for new pedagogical techniques and means of teaching. One of the directions for increasing the effectiveness of training, assimilation of information and reducing the costs of the learning process itself is the development and use of automated expert training systems. IN given time There are many terms for automated expert teaching system that are essentially the same thing.
The most popular of them are distance learning systems, computer training systems and others. To explain the full meaning of the terms listed above, the following definition can be given.
An expert training system (ETS) is a complex of software, hardware, educational and methodological tools built on the basis of the knowledge of subject matter experts (qualified teachers, methodologists, psychologists), implementing and controllinglearning process. The purpose of such a system is that, on the one hand, it helps the teacher to teach and control the student, and on the other hand, the student learns independently.
2. Purpose and objectives of the study, planned results
The purpose of the study is to develop a computer expert teaching system that will help increase the amount of acquired knowledge and the efficiency of information perception, as well as reduce the time spent studying the subject, including the time spent by the teacher on presenting information and instilling practical skills in students.
Main objectives of the study:
- Development of an ontological model of EOS;
- Development of the EOS structure;
- Justification and selection of computer implementation tools;
- Introduction of active components into the EOS (games, interactive systems, direct access to communication, for example, via Skype with the manager);
Object of study: expert training system.
Subject of study: models, structures and functions of EOS.
Scientific novelty consists of a new approach to EOS design based on modeling the learner’s activities and the use of artificial intelligence methods.
As part of the master's thesis, it is planned to obtain relevant scientific results in the following areas:
- Modeling learning processes.
- Designing the EOS structure for Internet and Intranet.
Planned results of the work: a prototype of an expert training system that will improve the quality of training and reduce training time.
3. Review of scientific research.
Since the issues of researching expert teaching systems and increasing the effectiveness of training in this system are an important part of solving complex problems using expert systems. EOS have been widely studied by both foreign and domestic specialists.
3.1. Review of international sources
First training system Plato based on a powerful computer from the company " Control Data Corporation "was developed in the USA in the late 50s and developed over 20 years. The creation and use of training programs have become truly widespread since the early 80s, when personal computers appeared and became widespread. Since then, educational applications of computers have become one of their main applications, along with word processing and graphics, pushing mathematical calculations into the background.
ECSI was also founded in 1972 and has since established itself as a leading service provider to the education industry. The company specializes in developing products and services to enhance the learning experience for students and their parents. ECSI currently serves more than 1,300 schools, colleges and universities across the country, offering a wide range of fully customized, intuitive learning systems.
3.2. Review of national sources
Modern training systems include TrainingWare, eLearning Server 3000 v2.0, eLearningOffice 3000, IBM Workplace Collaborative Learning and HyperMethod 3.5 from HyperMethod, which is the largest Russian developer of ready-made solutions and software in the field of multimedia, expert training and e-commerce.
4. Expert training systems
An expert learning system (ETS) is a computer program built on the basis of the knowledge of subject matter experts (qualified teachers, methodologists, psychologists) that carries out and controls the learning process. The purpose of such a system is that, on the one hand, it helps the teacher to teach and control the student, and on the other hand, the student learns independently.
The main components of the EOS are:
- knowledge base;
- output machine;
- knowledge extraction module;
- training module;
- explanation system;
- testing module.
Picture 1- Functional model of the EOS structure
(animation: 8 frames, 5 repetition cycles, 118 kilobytes)
In this model, the upper part of the EOS is inherited from the ES, and the lower part represents blocks that ensure the process of training and testing.
A knowledge base is a depository of knowledge modules. The knowledge module of expert systems is a formalized, using some method of knowledge representation (production system, frames, semantic networks, 1st order predicate calculus) display of objects of the subject area, their relationships, actions on objects.
Working with the knowledge base involves the following stages:
- extracting knowledge from experts;
- formalization of knowledge;
- access, processing of knowledge modules.
During the learning process, expert knowledge can be transferred to the learner in the form of a piece of information (text, graphic, multimedia), as well as knowledge based on experience, which cannot be transferred directly to the learner, but is acquired by him in the course of independent activity].
To transfer expert knowledge, developed hypertext technology is widely used - from traditional programs on creating help (help) to modern tools for creating and supporting Web sites (for example, Dreamweaver MX).
Unlike ES, to build the knowledge base of EOS, not only expert teachers are involved, but also knowledge about pedagogical techniques and teaching strategies and about psychological characteristics personality. Therefore, knowledge modules are formed by many experts. And here it is necessary to take into account the consistency of expert opinions and fine-tune the knowledge base, taking into account the competence of experts. Of course, these difficulties can be circumvented if there is an expert who combines the knowledge of a specialist in the subject area, knowledge of teaching tactics and strategies, and masters psychological teaching techniques, that is, a highly qualified teacher.
The training component is a set of software modules that implement various output mechanisms to achieve the pedagogical goal in training. EOS, unlike other computer teaching aids, are interactive: they have a dialogue with the student, which is very attractive for the latter.
The construction of dialogue is based on the basic psychological principles of learning:
- user-friendly interface;
- exit the dialogue at any time;
- timely and motivated assistance.
Each question asked of the student must be carefully thought through, and if necessary, provide a more detailed question in order to better understand it.
As a result of the study it has been shown that many components of creating an EOS depend on the outcome of the training, therefore, to create an EOS knowledge base, you need a specialist who has excellent knowledge in the subject area, and is also confident in teaching techniques.
5. Client-server technology of expert training system for networks InternetAndIntranet
The client-server architecture consists of the following components:
a server that fulfills client requests; client, which provides a user interface that sends requests to the server and receives responses from it; network communication software that communicates between a client and a server. The use of client-server technology provides certain advantages when building an ES: the knowledge base is stored on the server and, therefore, the need to update it is done once;
the knowledge base can be accessible to other applications; and the advantage for expert learning systems (ETS) is that you can store content on a server and track learning statistics on it.
Client-server ES and EOS for Internet/Intranet networks make it possible to expand the possibilities of their use in distance education.
Computer training systems allow both the development of ES prototypes and can be used for tailored testing and training of students over a local network.
The main components of the EOS are the following: knowledge base editor; logical inference machines (direct, inverse, indirect inference, Bayes formula); explanation subsystem; dough analyzer; teacher module; training component.
The main task of expert learning systems is to provide the student with the opportunity to acquire knowledge, skills, and abilities in developing knowledge bases and creating prototypes of electronic systems independently, as well as for trained testing.
There are at least five important reasons that hinder the implementation of client-server (distributed) ES:
- The structural elements of the ES components are not isolated from each other.
- A database is not a database, for which there are powerful DBMSs (Oracle, InterBase, MySQL, and so on) that use SQL queries.
- Multi-user access to the knowledge base for editing is simply not acceptable.
- The logical conclusion and the specifics of creating a knowledge base (different ways of representing knowledge) do not contribute to the need to combine them into a single system. A number of description languages and Web services have been developed for Symantec Web, but there are still no proposals for implementing logical inference.
- Software tools for building ES and knowledge bases are exclusive and expensive.
You can, of course, place the ES on a Web server for downloading to the client machine via the download link and update it on the server, but this is not a client-server solution.
Similarly, one can argue about the use of a three-tier client-server architecture (Server - CORBA - Client), when the knowledge base is located on the application server and is presented in the form of business decision rules.
Also not suitable for the “thin client” technology (KB, logical inference, explanation system are located on the server, and the dialogue with the ES is supported both on the server and on the client) and “thick client” (KB, logical inference, explanation system are located on the client machine, and the dialog interface is supported by the client and server).
Note that the knowledge base of the ES is intellectual property and may not be made available for free use. And educational KBs should be placed on a Web server so that any interested user can analyze how the ES works and improve their knowledge of the subject area.
We should not forget about server loads during peak situations. No provider will give away a server just for the functioning of an ES, since the user’s reaction during consultation or explanation is not predictable. And these are important aspects of the functioning of the ES (consultations can last from minutes to several hours).
Developing an EOS for Internet/Intranet networks is a completely different matter.
EOS is a computer system built on the basis of the knowledge of subject matter experts (qualified teachers, methodologists, psychologists), which carries out and controls the learning process. The purpose of such a system is that, on the one hand, it helps the teacher teach and control students, and on the other hand, students learn independently.
The main components of the EOS are the following: knowledge base; output machine; training module; explanation system; learning testing module.
As a rule, the knowledge base contains:
Psychodiagnostic rules for identifying psychological types of students.
Didactic techniques for teaching. The rules represent the accumulated knowledge of teachers for assessing students' knowledge.
Learning rules change the sequence of presented content tasks. This sequence is a function of many variables: the psychological type of the student, the level of training, the current response of the student, the level of difficulty of the task, the amount of training completed.
In connection with what has been said about distributed ES, it is recommended to use “thick client” technology for training and testing, that is, when all the components of the ES are located on the client machine, and the results of training and testing are transferred to the server. And there is no need to fear that the results can be replaced, given the modern encryption capabilities of the protocol with a remote server. Why this particular technology? It is known that about 80% of all information perceived by a person - it's visual. Therefore, multimedia technologies (avi files) are a priority in training. If you place them and run them onserver - this is a huge load on the server and, as a result, traffic increases to enormous sizes.
conclusions
EOS, unlike other computer learning technologies, have the ability to implement the learning process according to an individual student model. Learning with the help of ES is focused on the acquisition of knowledge by the learner himself. Namely, such specialists are in demand in the modern labor market. EOS also has its advantages and disadvantages.
The main disadvantages associated with expert learning systems can be divided into psychological associated with the lack of “live” communication with the teacher, high requirements for self-organization and technical, which are caused by imperfections in content, technology and telecommunications infrastructure.
The advantages of expert training systems are:
- Geographical and temporal advantages.
- Personalization of the learning process. Opportunity to train various categories of people, including those with disabilities.
- Expanding the information being studied and increasing the intensity of learning.
- Optimization and automation of the knowledge transfer process.
The master's thesis is devoted to the current scientific problem of automating an expert teaching system. As part of the research, the following was carried out:
- Existing expert training systems are analyzed.
- A study was carried out on an automated expert training system.
- The Client-server technology of an expert training system for Internet and Intranet networks is considered.
In accordance with the statement of the problem, the further direction of research is the selection, development and adaptation of an expert teaching system, its software implementation and testing.
At the time of writing this abstract, the master's thesis has not yet been completed. Final completion: December 2013. The full text of the work and materials on the topic can be obtained from the author or his supervisor after the specified date.
List of sources
1. Brooking A. Expert systems. Operating principles and examples: Transl. from English / A. Brooking, P. Jones; [Ed. R. Forsyth. - M.: Radio and communication, 1987. - 224 p.
2. - American Association for Artificial Intelligence American Association for Artificial Intelligence (AAAI).
7. Karpova I.P. Analysis of student responses in automated learning systems / I.P. Karpova // - Information Technologies, 2001, No. 11. - pp. 49-55.
8. Pusilovsky, P., Adaptive and Intelligent Technologies for Web-based Education. In C. Rollinger and C. Peylo (eds.), Special Issue on Intelligent Systems and Teleteaching, Konstliche Intelligenz, 4, 19 - 25.
9. Burdaev V.P. Client-server technology of an expert training system for Internet and Intranet networks. // Artificial intelligence.
11. Andreychikov A.V. Intelligent information systems. /A. V. Andreichikov, O. N. Andreichikova: Textbook. - M.: Finance and Statistics, 2004. - 424 p.
12. Atanov G. A. Training and artificial intelligence, or the foundations of modern didactics high school. /G. A. Atanov, I. N. Pustynnikova. - Donetsk: DOU, 2002. - 504 p.
13. Marvin Minsky. The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. 2007. - 332 p.
Expert system for training
is a software system that implements the learning function based on expert knowledge. EOS capabilities: Network presentation of training courses
Learner models
Generation of security questions and data for analysis of answers to them
Possibility of increasing knowledge bases, skills and abilities
Expert system tasks: provide the student with clear criteria for achieving educational goals (control system),
help him build an optimal individual training schedule.
save the results of previous consultations.
Expert system for solving problems in the subject area being studied
Expert system for diagnosing student errors
Expert system for planning the exercise management process
1. Teaching
Network presentation of training courses
Learner models
Generation of security questions and data for analysis of answers to them
Possibility of increasing knowledge bases, skills and abilities
Expert system tasks: provide the student with clear criteria for achieving educational goals (control system),
help him build an optimal individual training schedule.
save the results of previous consultations.
Expert system for solving problems in the subject area being studied
Expert system for diagnosing student errors
Expert system for planning the exercise management process
1. Teaching
provide the student with clear criteria for achieving educational goals (control system),
help him build an optimal individual training schedule.
save the results of previous consultations.
Expert system for solving problems in the subject area being studied
Expert system for diagnosing student errors
Expert system for planning the exercise management process
1. Teaching
1. Teaching . Creating an environment for knowledge acquisition.
2. Education. Performing the functions of a teacher in presenting the material, monitoring its assimilation and diagnosing errors
3. Monitoring and diagnostics . Providing test questions, evaluating answers and identifying errors.
4. Training . Creating an environment that allows you to acquire and consolidate the required skills and abilities.
Expert Shell
Expert Shell
designed to organize training in the “computer-student” mode. Training as part of the information and educational environment “Chopin” takes place individually curriculum and at your own pace. The expert shell in the environment plays the role of an adviser who, based on the student’s real achievements recorded in the database of testing and training results, builds a training plan and makes decisions about the student achieving a certain level of knowledge about the subject area. VIPES – hybrid shell 
VIPES is designed to work online. This shell is multi-user. This system uses a graphical user interface. Subject specialists and teachers are able to independently create and edit knowledge bases for the VIPES shell.
Test Shell
Data Analysis Console
Multi-user ES shell with a visual interface
Training and testing database
File system for test and training course data
Learning Shell
Service module
Testing of initial data
Test Shell
Data Analysis Console
Multi-user ES shell with a visual interface
Training and testing database
File system for test and training course data
Learning Shell
Service module
Testing of initial data includes verification of factual information that serves as the basis for the examination.
Logical testing of the knowledge base consists in detecting logical errors in the production system that do not depend on the subject area; missing and overlapping rules; inconsistent and terminal clauses (inconsistent conditions).
Concept testing
is carried out to check the general structure of the system and take into account all aspects of the problem being solved. 
1. Simplicity of solving the initial problem of building a system.
2. Possibility of adding to the testing system during use.
3. Enough simple circuit practical use.
4. Attractiveness for the user due to the time and effort spent on testing knowledge. 
offering several answer options indirectly encourages the user to analyze various solutions and explore the task in more depth.
Reviewing expert system.
One of the ways to solve the problem of intensifying the educational process is to use the latest information technologies in the training and internship of young specialists.
To solve this problem, a project has been developed to create a reviewing expert system that performs the functions of an expert - consultant and teacher at the same time.
An expert system is a program that is designed to simulate human intelligence, experience, and the process of cognition.
With an expert system based on a peer-review approach, the user provides more data as well as his or her own solution or course of action.
The system evaluates the user's plan and provides critical analysis.
Critical analysis includes alternatives, explanations, justifications, warnings and Additional information for consideration. 
The reviewing expert system implements two types of abilities: The system can function like a conventional expert system
The system can analyze any of the possible plans proposed by the user in the context of a scenario of possible actions, and produce a practical critical analysis.
The system can function like a conventional expert system
The system can analyze any of the possible plans proposed by the user in the context of a scenario of possible actions, and produce a practical critical analysis.
1. The user enters information regarding the current action and submits his operating plan or set of actions.
2. the entered data is analyzed
3. the user gets the required result.
4. If the user has specified an action plan as unknown, the reviewing expert system will function as a regular expert system and will produce a plan recommended by the expert. 
All expert systems perform different functions, but they pursue one single goal - to compare a given task with the available information in the database and perform the function that the given expert system performs.
What is an expert-learning system?
What are the 3 aspects of expert system testing?
-
Abstract on the topic:
"Creating a report as a database object. Expert and learning systems"
Contents
Creating a report as a database object
Report structure in Design mode
Methods for creating a report
Create a report
Creating a report as a database object
A report is a formatted representation of data that is displayed on screen, printed, or in a file. They allow you to extract the necessary information from the database and present it in a form that is easy to understand, and also provide ample opportunities for summarizing and analyzing data.
When printing tables and queries, information is displayed practically in the form in which it is stored. There is often a need to present data in the form of reports that have a traditional look and are easy to read. A detailed report includes all the information from a table or query, but contains headers and is broken into pages with headers and footers.
Report structure in Design mode
Microsoft Access displays data from a query or table in a report, adding text elements to make it easier to read.
These elements include:
Title. This section is printed only at the top of the first page of the report. Used to output data, such as report title text, a date, or a statement of document text, that should be printed once at the beginning of the report. To add or remove a report title area, select the Report Title/Note command from the View menu.
Page header. Used to display data such as column headings, dates, or page numbers printed at the top of each report page. To add or remove a header, select Header and Footer from the View menu. Microsoft Access adds a header and footer at the same time. To hide one of the headers and footers, you need to set its Height property to 0.
The data area located between the header and footer of a page. Contains the main text of the report. This section displays the data printed for each of the records in the table or query on which the report is based. To place controls in the data area, use a list of fields and a toolbar. To hide the data area, you need to set the section's Height property to 0.
Footer. This section appears at the bottom of every page. Used to display data, such as totals, dates, or page numbers, printed at the bottom of each report page.
Note. Used to output data, such as conclusion text, grand totals, or a caption, that should be printed once at the end of the report. Although the report Note section is at the bottom of the report in Design view, it is printed above the page footer on the last page of the report. To add or remove a report notes area, select the Report Title/Note command from the View menu. Microsoft Access simultaneously adds and removes the title and comment areas of a report.
Methods for creating a report
You can create reports in Microsoft Access in a variety of ways:
Constructor
Report Wizard
Auto report: to column
Auto report: tape
Chart Wizard
Postal labels
The wizard allows you to create reports with grouping of records and represents the simplest way creating reports. It puts the selected fields into the report and offers six report styles. After completing the Wizard, the resulting report can be modified in Design mode. Using the Auto Report feature, you can quickly create reports and then make some changes to them.
To create an Auto Report, you must perform the following steps:
In the database window, click the Reports tab and then click the Create button. The New Report dialog box appears.
Select the Autoreport: column or Autoreport: strip item in the list.
In the data source field, click the arrow and select Table or Query as the data source.
Click on the OK button.
The Auto Report Wizard creates an auto report in a column or strip (user's choice) and opens it in Preview mode, which allows you to see what the report will look like when printed.
Changing the report display scale
To change the display scale, use the pointer - a magnifying glass. To see the entire page, you must click anywhere on the report. The report page will be displayed on a reduced scale.
Click on the report again to return to a larger view. In the enlarged report view, the point you clicked on will be in the center of the screen. To scroll through report pages, use the navigation buttons at the bottom of the window.
Print a report
To print a report, do the following:
On the File menu, click on the Print command.
In the Print area, click the Pages option.
To print only the first page of the report, enter 1 in the From field and 1 in the To field.
Click on the OK button.
Before printing a report, it is advisable to view it in Preview mode, to access which you need to select Preview from the View menu.
If you print with a blank page at the end of your report, make sure that the Height setting for report notes is set to 0. If you print with blank pages in between, make sure that the sum of the form or report width and the left and right margin widths does not exceed the width of the sheet of paper specified in the Page Setup dialog box (File menu).
When designing report layouts, use the following formula: report width + left margin + right margin
In order to adjust the size of the report, you must use the following techniques:
change the report width value;
Reduce margin width or change page orientation.
Create a report
1. Launch Microsoft Access. Open the database (for example, the educational database "Dean's Office").
2. Create an AutoReport: tape, using a table as a data source (for example, Students). The report opens in Preview mode, which allows you to see what the report will look like when printed.
3. Switch to Design mode and edit and format the report. To switch from Preview mode to Design mode, you must click Close on the Access application window toolbar. The report will appear on the screen in Design mode.
Editing:
1) remove the student code fields in the header and data area;
2) move all fields in the header and data area to the left.
3) Change the text in the page title
In the Report Title section, select Students.
Place the mouse pointer to the right of the word Students so that the pointer changes to a vertical bar (the input cursor) and click at that position.
Enter NTU "KhPI" and press Enter.
4) Move the Caption. In the Footer, select the =Now() field and drag it to the Report Header under the name Students. The date will appear below the title.
5) On the Report Designer toolbar, click the Preview button to preview the report.
Formatting:
1) Select the heading Students of NTU "KhPI"
2) Change the typeface, font style and color, as well as the background fill color.
3) On the Report Designer toolbar, click the Preview button to preview the report.
Style change:
To change the style, do the following:
On the Report Designer toolbar, click the AutoFormat button to open the AutoFormat dialog box.
In the Report - AutoFormat Object Styles list, click Strict and then click OK. The report will be formatted in the Strict style.
Switches to Preview mode. The report will be displayed in the style you selected. From now on, all reports created using the AutoReport function will have the Strict style until you specify a different style in the AutoFormat window.
Expert and learning systems
Expert systems are one of the main applications of artificial intelligence. Artificial intelligence is one of the branches of computer science that deals with the problems of hardware and software modeling of those types of human activities that are considered intellectual.
The results of research on artificial intelligence are used in intelligent systems that are capable of solving creative problems belonging to a specific subject area, knowledge about which is stored in the memory (knowledge base) of the system. Artificial intelligence systems are focused on solving a large class of problems, which include the so-called partially structured or unstructured tasks (weakly formalizable or unformalizable tasks).
Information systems used to solve semi-structured problems are divided into two types:
Creating management reports (performing data processing: searching, sorting, filtering). Decisions are made based on the information contained in these reports.
Developing possible solution alternatives. Decision making comes down to choosing one of the proposed alternatives.
Information systems that develop solution alternatives can be model or expert:
Model information systems provide the user with models (mathematical, statistical, financial, etc.) that help ensure the development and evaluation of solution alternatives.
Expert information systems provide the development and assessment of possible alternatives by the user through the creation of systems based on knowledge obtained from specialist experts.
Expert systems are computer programs that accumulate the knowledge of specialists - experts in specific subject areas, which are designed to obtain acceptable solutions in the process of information processing. Expert systems transform the experience of experts in any particular field of knowledge into the form of heuristic rules and are intended for consultation of less qualified specialists.
It is known that knowledge exists in two forms: collective experience and personal experience. If a subject area is represented by collective experience (for example, higher mathematics), then this subject area does not need expert systems. If in a subject area most of the knowledge is the personal experience of high-level specialists and this knowledge is weakly structured, then such an area needs expert systems. Modern expert systems have found wide application in all spheres of the economy.
The knowledge base is the core of the expert system. The transition from data to knowledge is a consequence of the development of information systems. Databases are used to store data, and knowledge bases are used to store knowledge. Databases, as a rule, store large amounts of data with a relatively low cost, while knowledge bases store small but expensive information sets.
A knowledge base is a body of knowledge described using the selected form of its presentation. Filling the knowledge base is one of the most difficult tasks, which is associated with the selection of knowledge, its formalization and interpretation.
The expert system consists of:
knowledge base (as part of working memory and a rule base), designed for storing initial and intermediate facts in working memory (also called a database) and storing models and rules for manipulating models in the rule base
problem solver (interpreter), which provides the implementation of a sequence of rules for solving a specific problem based on facts and rules stored in databases and knowledge bases
explanation subsystem allows the user to get answers to the question: “Why did the system make this decision?”
a knowledge acquisition subsystem designed to both add new rules to the knowledge base and modify existing rules.
user interface, a set of programs that implement the user’s dialogue with the system at the stage of entering information and obtaining results.
Expert systems differ from traditional data processing systems in that they typically use symbolic representation, symbolic inference, and heuristic search for solutions. For solving weakly formalizable or non-formalizable problems, neural networks or neurocomputers are more promising.
The basis of neurocomputers is made up of neural networks - hierarchical organized parallel connections of adaptive elements - neurons, which ensure interaction with objects of the real world in the same way as the biological nervous system.
Great successes in the use of neural networks have been achieved in the creation of self-learning expert systems. The network is configured, i.e. train by passing all known solutions through it and achieving the required answers at the output. The setup consists of selecting the parameters of the neurons. Often they use a specialized training program that trains the network. After training, the system is ready for operation.
If in an expert system its creators pre-load knowledge in a certain form, then in neural networks it is unknown even to the developers how knowledge is formed in its structure in the process of learning and self-learning, i.e. the network is a "black box".
Neurocomputers, as artificial intelligence systems, are very promising and can be endlessly improved in their development. Currently, artificial intelligence systems in the form of expert systems and neural networks are widely used in solving financial and economic problems.
What is an expert-learning system?
What are the 3 aspects of expert system testing?
Abstract on the topic:
"Creating a report as a database object. Expert and learning systems"
Contents
Creating a report as a database object
Report structure in Design mode
Methods for creating a report
Create a report
Creating a report as a database object
A report is a formatted representation of data that is displayed on screen, printed, or in a file. They allow you to extract the necessary information from the database and present it in a form that is easy to understand, and also provide ample opportunities for summarizing and analyzing data.
When printing tables and queries, information is displayed practically in the form in which it is stored. There is often a need to present data in the form of reports that have a traditional look and are easy to read. A detailed report includes all the information from a table or query, but contains headers and is broken into pages with headers and footers.
Report structure in Design mode
Microsoft Access displays data from a query or table in a report, adding text elements to make it easier to read.
These elements include:
Title. This section is printed only at the top of the first page of the report. Used to output data, such as report title text, a date, or a statement of document text, that should be printed once at the beginning of the report. To add or remove a report title area, select the Report Title/Note command from the View menu.
Page header. Used to display data such as column headings, dates, or page numbers printed at the top of each report page. To add or remove a header, select Header and Footer from the View menu. Microsoft Access adds a header and footer at the same time. To hide one of the headers and footers, you need to set its Height property to 0.
The data area located between the header and footer of a page. Contains the main text of the report. This section displays the data printed for each of the records in the table or query on which the report is based. To place controls in the data area, use a list of fields and a toolbar. To hide the data area, you need to set the section's Height property to 0.
Footer. This section appears at the bottom of every page. Used to display data, such as totals, dates, or page numbers, printed at the bottom of each report page.
Note. Used to output data, such as conclusion text, grand totals, or a caption, that should be printed once at the end of the report. Although the report Note section is at the bottom of the report in Design view, it is printed above the page footer on the last page of the report. To add or remove a report notes area, select the Report Title/Note command from the View menu. Microsoft Access simultaneously adds and removes the title and comment areas of a report.
Methods for creating a report
You can create reports in Microsoft Access in a variety of ways:
Constructor
Report Wizard
Auto report: to column
Auto report: tape
Chart Wizard
Postal labels
The wizard allows you to create reports with grouping of records and represents the simplest way creating reports. It puts the selected fields into the report and offers six report styles. After completing the Wizard, the resulting report can be modified in Design mode. Using the Auto Report feature, you can quickly create reports and then make some changes to them.
To create an Auto Report, you must perform the following steps:
In the database window, click the Reports tab and then click the Create button. The New Report dialog box appears.
Select the Autoreport: column or Autoreport: strip item in the list.
In the data source field, click the arrow and select Table or Query as the data source.
Click on the OK button.
The Auto Report Wizard creates an auto report in a column or strip (user's choice) and opens it in Preview mode, which allows you to see what the report will look like when printed.
Changing the report display scale
To change the display scale, use the pointer - a magnifying glass. To see the entire page, you must click anywhere on the report. The report page will be displayed on a reduced scale.
Click on the report again to return to a larger view. In the enlarged report view, the point you clicked on will be in the center of the screen. To scroll through report pages, use the navigation buttons at the bottom of the window.
Print a report
To print a report, do the following:
On the File menu, click on the Print command.
In the Print area, click the Pages option.
To print only the first page of the report, enter 1 in the From field and 1 in the To field.
Click on the OK button.
Before printing a report, it is advisable to view it in Preview mode, to access which you need to select Preview from the View menu.
If you print with a blank page at the end of your report, make sure that the Height setting for report notes is set to 0. If you print with blank pages in between, make sure that the sum of the form or report width and the left and right margin widths does not exceed the width of the sheet of paper specified in the Page Setup dialog box (File menu).
When designing report layouts, use the following formula: report width + left margin + right margin
In order to adjust the size of the report, you must use the following techniques:
change the report width value;
Reduce margin width or change page orientation.
Create a report
1. Launch Microsoft Access. Open the database (for example, the educational database "Dean's Office").
2. Create an AutoReport: tape, using a table as a data source (for example, Students). The report opens in Preview mode, which allows you to see what the report will look like when printed.
3. Switch to Design mode and edit and format the report. To switch from Preview mode to Design mode, you must click Close on the Access application window toolbar. The report will appear on the screen in Design mode.
Editing:
1) remove the student code fields in the header and data area;
2) move all fields in the header and data area to the left.
3) Change the text in the page title
In the Report Title section, select Students.
Place the mouse pointer to the right of the word Students so that the pointer changes to a vertical bar (the input cursor) and click at that position.
Enter NTU "KhPI" and press Enter.
4) Move the Caption. In the Footer, select the =Now() field and drag it to the Report Header under the name Students. The date will appear below the title.
5) On the Report Designer toolbar, click the Preview button to preview the report.
Formatting:
1) Select the heading Students of NTU "KhPI"
2) Change the typeface, font style and color, as well as the background fill color.
3) On the Report Designer toolbar, click the Preview button to preview the report.
Style change:
To change the style, do the following:
On the Report Designer toolbar, click the AutoFormat button to open the AutoFormat dialog box.
In the Report - AutoFormat Object Styles list, click Strict and then click OK. The report will be formatted in the Strict style.
Switches to Preview mode. The report will be displayed in the style you selected. From now on, all reports created using the AutoReport function will have the Strict style until you specify a different style in the AutoFormat window.
Expert and learning systems
Expert systems are one of the main applications of artificial intelligence. Artificial intelligence is one of the branches of computer science that deals with the problems of hardware and software modeling of those types of human activities that are considered intellectual.
The results of research on artificial intelligence are used in intelligent systems that are capable of solving creative problems belonging to a specific subject area, knowledge about which is stored in the memory (knowledge base) of the system. Artificial intelligence systems are focused on solving a large class of problems, which include the so-called partially structured or unstructured tasks (weakly formalizable or unformalizable tasks).
Information systems used to solve semi-structured problems are divided into two types:
Creating management reports (performing data processing: searching, sorting, filtering). Decisions are made based on the information contained in these reports.
Developing possible solution alternatives. Decision making comes down to choosing one of the proposed alternatives.
Information systems that develop solution alternatives can be model or expert:
Model information systems provide the user with models (mathematical, statistical, financial, etc.) that help ensure the development and evaluation of solution alternatives.
Expert information systems provide the development and assessment of possible alternatives by the user through the creation of systems based on knowledge obtained from specialist experts.
Expert systems are computer programs that accumulate the knowledge of specialists - experts in specific subject areas, which are designed to obtain acceptable solutions in the process of information processing. Expert systems transform the experience of experts in any particular field of knowledge into the form of heuristic rules and are intended for consultation of less qualified specialists.
It is known that knowledge exists in two forms: collective experience and personal experience. If a subject area is represented by collective experience (for example, higher mathematics), then this subject area does not need expert systems. If in a subject area most of the knowledge is the personal experience of high-level specialists and this knowledge is weakly structured, then such an area needs expert systems. Modern expert systems have found wide application in all spheres of the economy.
The knowledge base is the core of the expert system. The transition from data to knowledge is a consequence of the development of information systems. Databases are used to store data, and knowledge bases are used to store knowledge. Databases, as a rule, store large amounts of data with a relatively low cost, while knowledge bases store small but expensive information sets.
A knowledge base is a body of knowledge described using the selected form of its presentation. Filling the knowledge base is one of the most difficult tasks, which is associated with the selection of knowledge, its formalization and interpretation.
The expert system consists of:
knowledge base (as part of working memory and a rule base), designed for storing initial and intermediate facts in working memory (also called a database) and storing models and rules for manipulating models in the rule base
problem solver (interpreter), which provides the implementation of a sequence of rules for solving a specific problem based on facts and rules stored in databases and knowledge bases
explanation subsystem allows the user to get answers to the question: “Why did the system make this decision?”
a knowledge acquisition subsystem designed to both add new rules to the knowledge base and modify existing rules.
user interface, a set of programs that implement the user’s dialogue with the system at the stage of entering information and obtaining results.
Expert systems differ from traditional data processing systems in that they typically use symbolic representation, symbolic inference, and heuristic search for solutions. For solving weakly formalizable or non-formalizable problems, neural networks or neurocomputers are more promising.
The basis of neurocomputers is made up of neural networks - hierarchical organized parallel connections of adaptive elements - neurons, which ensure interaction with objects of the real world in the same way as the biological nervous system.
Great successes in the use of neural networks have been achieved in the creation of self-learning expert systems. The network is configured, i.e. train by passing all known solutions through it and achieving the required answers at the output. The setup consists of selecting the parameters of the neurons. Often they use a specialized training program that trains the network. After training, the system is ready for operation.
If in an expert system its creators pre-load knowledge in a certain form, then in neural networks it is unknown even to the developers how knowledge is formed in its structure in the process of learning and self-learning, i.e. the network is a "black box".
Neurocomputers, as artificial intelligence systems, are very promising and can be endlessly improved in their development. Currently, artificial intelligence systems in the form of expert systems and neural networks are widely used in solving financial and economic problems.